AUTHOR=Zhu Xiaoxia , Zhu Zhixin , Gu Lanfang , Chen Liang , Zhan Yancen , Li Xiuyang , Huang Cheng , Xu Jiangang , Li Jie 

TITLE=Prediction models and associated factors on the fertility behaviors of the floating population in China

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.977103

DOI=10.3389/fpubh.2022.977103

ISSN=2296-2565

ABSTRACT=The floating population size has grown rapidly in China, and their fertility behaviors affect the management and development of cities. Based on the data set of the China Migrants Dynamic Survey in 2016, the Logistic regression model and multiple linear regression models were established to explore the related factors of fertility behaviors among the floating population. The artificial neural network model, the Naive Bayes model and the Logistic regression model were used for prediction. The findings showed that gender, household registration, education level, the duration of settlement and scope of migration, housing and economic conditions, and health services all affected the reproductive behavior of the floating population. Among them, the improvement of social service level, more than 10 years of post-migration residence and family economic conditions positively impacted their fertility behavior. Non-agricultural migrants with college degrees or above who live in first-tier cities were less likely to have children and were more likely to delay childbearing. Among the prediction models, the artificial neural network model and the Logistic regression model had better prediction effects. Expanding the scope of social services, improving the employment and income of immigrants, and introducing preferential housing policies might improve the probability of the floating population bearing children, and promote economic development. The artificial neural network and Logistic regression model could predict individual fertility behavior and provide a scientific basis for urban population management.